{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets\n",
    "\"\"\"绘制决策边界的函数\"\"\"\n",
    "def plot_decision_boundary(model, axis):\n",
    "    x0, x1 = np.meshgrid(\n",
    "        np.linspace(axis[0], axis[1], int((axis[1]-axis[0])*100)).reshape(-1, 1),\n",
    "        np.linspace(axis[2], axis[3], int((axis[3]-axis[2])*100)).reshape(-1, 1)\n",
    "    )\n",
    "    X_new = np.c_[x0.ravel(), x1.ravel()]\n",
    "    y_predict = model.predict(X_new)\n",
    "    zz = y_predict.reshape(x0.shape)\n",
    "\n",
    "    from matplotlib.colors import ListedColormap\n",
    "    custom_cmap = ListedColormap(['#EF9A9A', '#FFF59D', '#90CAF9'])\n",
    "\n",
    "    plt.contourf(x0, x1, zz, cmap=custom_cmap)\n",
    "\n",
    "iris=datasets.load_iris()\n",
    "X=iris.data[:,:2]#所有数据，挑选前两个特征\n",
    "y=iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(X,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 15.6 ms\n",
      "Wall time: 16.3 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8666666666666667"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\"\"\"OVR\"\"\"\n",
    "from sklearn.linear_model import LogisticRegression #sklearn中的LogisticRegression默认支持多分类任务,这里设置OVR\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "log_reg=LogisticRegression()\n",
    "ovr_log_reg=OneVsRestClassifier(log_reg)\n",
    "ovr_log_reg.fit(x_train,y_train)\n",
    "ovr_log_reg.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(ovr_log_reg,axis=[4,8.5,1.5,4.5])\n",
    "plt.scatter(X[y==0,0],X[y==0,1])\n",
    "plt.scatter(X[y==1,0],X[y==1,1])\n",
    "plt.scatter(X[y==2,0],X[y==2,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 15.6 ms\n",
      "Wall time: 12 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8666666666666667"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\"\"\"使用OVO，更快\"\"\"\n",
    "from sklearn.multiclass import OneVsOneClassifier\n",
    "log_reg2=LogisticRegression(solver=\"newton-cg\")#默认multi_class=\"multinomial\"\n",
    "OVO_log_reg2=OneVsOneClassifier(log_reg2)\n",
    "OVO_log_reg2.fit(x_train,y_train)\n",
    "OVO_log_reg2.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(OVO_log_reg2,axis=[4,8.5,1.5,4.5])\n",
    "plt.scatter(X[y==0,0],X[y==0,1])\n",
    "plt.scatter(X[y==1,0],X[y==1,1])\n",
    "plt.scatter(X[y==2,0],X[y==2,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用所有数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=iris.data\n",
    "y=iris.target\n",
    "x_train,x_test,y_train,y_test=train_test_split(X,y,test_size=0.3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9333333333333333"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_log_reg=LogisticRegression(solver=\"newton-cg\")\n",
    "ovo_log_reg=OneVsOneClassifier(base_log_reg)\n",
    "ovr_log_reg.fit(x_train,y_train)\n",
    "ovr_log_reg.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n也可以在声明LogisticRegression中设置参数使用muti_class=\"ovr\"或者\"multinomial\"对应Ovr与ovo\\n'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "也可以在声明LogisticRegression中设置参数使用muti_class=\"ovr\"或者\"multinomial\"对应Ovr与ovo\n",
    "\"\"\""
   ]
  }
 ],
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